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    <title>Cancer Regulome :: Software</title>

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                <h2>Applications</h2>
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            <div>
                <h4>Regulome Explorer</h4>
                <div>
                    This web-based tool provides for the exploration of data from TCGA. It can be used to explore the following:
                    <ul>
                        <li>The cancer comparison data sets which give information about common gene disruptions across different cancers.</li>
                        <li>Associations among multiple types of features (e.g., gene expression, methylation, copy number variation, clinical), computed using
                            random forest regression and displayed within an interactive Circvis tool.</li>
                        <li>Individual genome aberrations identified through the analysis of structural variations using FastBreak.</li>
                    </ul>
                    Regulome Explorer is undergoing a design review. Several aspects will be enhanced, including data services, user interface, and
                    interoperability with external tools. Entire new functionalities are also slated to be introduced. Supporting collaboration among users will be
                    a key focus. Persistence of analytical tasks and workflows will enable users to search through the large analytical result sets for potentially
                    novel feature associations.
                    <br/>
                    <br/>
                    Read more about Regulome Explorer in its <a href="http://regulome-explorer.googlecode.com" target="_blank">open source project</a>.
                    <br/>
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                <h4>Pubcrawl</h4>
                <div>
                    This tool provides a way to view a gene network based on literature distances in medline, and protein domain interactions. We have extended the
                    application for TCGA specific datasets to explore the associations between genes found in our pairwise and RF-ACE analyses, and view the
                    relative mutation levels of genes for a selected set of patients.
                    <br/>
                    <br/>
                    Read more about Pubcrawl in its <a href="http://pubcrawl.googlecode.com" target="_blank">open source project</a>.
                    <br/>
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                <h4>GeneSpot</h4>
                <div>
                    This tool provides a way to view a TCGA data from a gene-centric point-of-view.  It includes a number of interactive visualizations, and allows
                    the user to save their current exploration.  This application also enables the user to select specific cancers and genes of interest, and load
                    data that is generated from a variety of TCGA analysis.
                    <br/>
                    <br/>
                    Read more about GeneSpot in its <a href="http://github.com/cancerregulome/GeneSpot" target="_blank">open source project</a>.
                    <br/>
                </div>
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            <hr/>
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                <h4>Colorectal Cancer Aggressiveness Explorer</h4>
                <div>
                    The CRC Aggressiveness Explorer allows the exploration of molecular signatures associated with aggressive CRC. A red data point implies that
                    the signature is more prevalent in tumors with aggressive colorectal cancer, while a blue data point indicates the opposite. A color gradient
                    is used for the strength of association.
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                    <h4>Web Applications</h4>
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                <ul>
                    <li>
                        <h8><a href="http://explorer.cancerregulome.org" target="_blank">Regulome Explorer</a></h8>
                        <ul>
                            <li><a href="http://explorer.cancerregulome.org/all_pairs/" target="_blank">All Pairs Analysis</a></li>
                            <li><a href="http://explorer.cancerregulome.org/re/" target="_blank">Random Forest Analysis</a></li>
                        </ul>
                    </li>
                    <li><a href="http://explorer.cancerregulome.org/crc_agg/" target="_blank">CRC Aggressiveness Explorer</a></li>
                    <li><a href="http://explorer.cancerregulome.org/pubcrawl/" target="_blank">Pubcrawl</a></li>
                    <li><a href="http://genespot.cancerregulome.org/" target="_blank">GeneSpot</a></li>
                    <li><a href="http://dodoma.systemsbiology.net" target="_blank">DoDoMa</a></li>
                </ul>
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                        <h2>Visualizations</h2>
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                    <div>
                        We have created a variety of interactive visualizations and placed them within a library called Visquick. Visquick is a suite of
                        visualizations that are rendered in a modern web browser. It includes a set of core functionalities with each tool. The configuration of a
                        visualization can be defined dynamically, including size, color, layout, and interaction behavior. Hovercards are displayed whenever the
                        cursor is placed over a data point within a tool. Additional information about the data point is then provided, as well as links to
                        external websites containing contextual information relating to the selected piece of data.
                        <br/>
                        <br/>
                        Read more about Visquick in its <a href="http://visquick.googlecode.com" target="_blank">open source project</a>.
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                <h2>Infrastructure</h2>
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                <h4>Cloud Services</h4>
                We have explored the use of cloud technologies to deploy secure versions of Regulome Explorer and Pubcrawl. These HTML5 web applications
                are deployed to <a href="https://developers.google.com/appengine" target="_blank">Google App Engine</a> and
                <a href="http://aws.amazon.com" target="_blank">Amazon Web Services</a> using Addama (see below). User authentication is managed by an OpenID
                provider, and secure API keys are generated by Addama for programmatic access by computational researchers. Data exploration and query capabilities
                to relational and graph databases, and search indexes are provided through standardized REST+JSON programming interfaces.
                <br/>
                <img src="images/infrastructure1.png" alt="Infrastructure">
            </div>
            <hr/>
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                <h4>Addama</h4>
                <div>
                    The Adaptive Data Management Service Architecture (or Addama) has been designed to support the needs of researchers at ISB. Its main goals are
                    to support ad-hoc software development within the research enterprise, to facilitate the integration of large heterogeneous data, and to
                    provide standardized interfaces for a variety of data management systems (e.g. relational databases, search engines, graph databases). Addama
                    provides REST+JSON web services for use by web applications, visualizations, and from the command-line by computational researchers. It also
                    offers a platform for the rapid development and deployment of HTML5 web applications using security and authentication protocols (OpenID)
                    provided by <a href="https://developers.google.com/appengine" target="_blank">Google App Engine</a>.
                    <br/>
                    <br/>
                    Read more about Addama in its <a href="http://addama.googlecode.com" target="_blank">open source project</a>.
                </div>
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            <hr/>
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                <h4>Golem</h4>
                <div>
                    Golem (Go Launch On Every Machine) is a simple and lightweight open source system for distributing and load balancing analysis jobs across many
                    machines. Using Golem on our internal server cluster, an exhaustive search for relationships among sample features that requires tens of
                    thousands of machine hours can be accomplished in a few hours of real time. Its focus on simplicity makes it easy for researchers to
                    parallelize analysis written in any language and we have used it to speed up many tasks including pairwise correlations, multivariate machine
                    learning using random forest, and structural variation analysis using Fastbreak.
                    <br/>
                    <br/>
                    Read more about Golem in its <a href="http://golem.googlecode.com" target="_blank">open source project</a>.
                </div>
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            <hr/>
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                <h4>Hukilau</h4>
                 <div>
                     Our analysis has created numerous types of associations between features in TCGA data. <b>Graph databases</b> are enabling us to navigate the
                     resulting multi-relational networks. A general framework, <a href="http://hukilau.googlecode.com" target="_blank">Hukilau</a>, is being
                     developed to integrate these databases. It currently supports the <a href="http://neo4j.org" target="_blank">Neo4j graph database</a>. Hukilau
                     provides scripts to load nodes and relationships into a Neo4j graph database, and a web service interface to query nodes and relationships.
                     <br/>
                     <br/>
                     Read more about Hukilau in its <a href="http://hukilau.googlecode.com" target="_blank">open source project</a>.
                </div>
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                        <h2>Data Analysis</h2>
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                    <div>
                        <h4>Fastbreak</h4>
                        <div>
                            This application has been designed to quickly screen for structural variation. We have now expanded Fastbreak to begin looking for
                            patterns of structural variation across cancer types. A paper covering the Fastbreak algorithm is submitted for publication.
                            <br/>
                            <br/>
                            Read more about Fastbreak in its <a href="http://fastbreak.googlecode.com" target="_blank">open source project</a>.
                        </div>
                    </div>
                    <hr/>
                    <div>
                        <h4>RF-ACE</h4>
                        <div>
                            This program is an efficient C++ implementation of a robust machine learning algorithm for uncovering multivariate associations from
                            large and diverse data sets. RF-ACE natively handles numerical and categorical data with missing values, and potentially large
                            quantities of noninformative features.
                            <br/>
                            <br/>
                            Read more about RF-ACE in its <a href="http://rf-ace.googlecode.com" target="_blank">open source project</a>.
                        </div>
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                    <hr/>
                    <div>
                        <h4>Dodoma</h4>
                        <div>
                            This software package and companion web application automatically identifies transcription factors with matching DNA binding domains,
                            and returns all associated position weight matrix (PWM) identifiers from JASPAR, TRANSFAC, and UniPROBE databases.
                            <br/>
                            <br/>
                            Read more about DoDoMa in its <a href="http://dodoma.googlecode.com" target="_blank">open source project</a>.
                        </div>
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